Download intrusion detection - FSU Computer Science

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts
no text concepts found
Transcript
INTRUSION DETECTION INTRUSION
DETECTION INTRUSION
DETECTION
INTRUSION DETECTION
INTRUSION DETECTION
INTRUSION DETECTION
INTRUSION DETECTION
INTRUSION
DETECTION
DETECTION
INTRUSION
DETECTION
INTRUSION DETECTION INTRUSION
DETECTION
INTRUSION DETECTION
INTRUSION DETECTION
INTRUSION
INTRUSION
DETECTION
DETECTION
INTRUSION DETECTION INTRUSION
DETECTION INTRUSION
DETECTION
INTRUSION DETECTION
INTRUSION
DETECTION
INTRUSION DETECTION
INTRUSION DETECTION
INTRUSION
DETECTION
DETECTION
INTRUSION
DETECTION
INTRUSION DETECTION
INTRUSION
DETECTION
INTRUSION
DETECTION
A
presentation over term paper
on
•Anomaly Detection
intrusion detection
•Misuse Detection
by
anuja jain
(MS in computer science)
monica achury
(MS in computer science)
Definition
INTRUSION
- The potential possibility of a deliberate
unauthorized attempt to:
•Access information
•Manipulate information
•Render a system unreliable or unusable
INTRUSION DETECTION
- The process of identifying and responding
to intrusion activities
Types of Intrusion
There are six types of Intrusions
•Attempted break-ins
•Masquerade attacks
•Penetration of the security control system
•Leakage
•Denial of service
•Malicious use
Intrusion Detection Techniques
•Anomaly Detection
Static
Dynamic
•Misuse Detection
Ex:- NIDES, MIDAS, STAT
Anomaly Detection Systems
• Statistical approaches
Tripwire, Self/Non-self
• Dynamic /Predictive pattern generation
NIDES, Pattern Matching (UNM)
Anomaly Detection
activity
measures
90
80
70
60
50
40
30
20
10
0
probable
intrusion
normal profile
abnormal
CPU
Process
Size
Relatively high false positive rate anomalies can just be new normal activities.
Misuse Detection Systems
•Expert Systems
•Keystroke Monitoring
•Model Based Intrusion Detection
Misuse Detection
pattern
matching
Intrusion
Patterns
intrusion
activities
Example: if (src_ip == dst_ip) then “land attack”
Can’t detect new attacks
IDS Design
Components of IDS
system activities are
observable
Audit Records
Audit Data
Preprocessor
Activity Data
Detection
Models
Detection Engine
normal and intrusive
activities have distinct
evidence
Alarms
Decision
Table
Decision Engine
Action/Report
Important Features
•Fault tolerant.
•Minimum human supervision.
•Resist subversion.
•Minimal Overhead.
•Platform Independent
Continued…
•Adaptable.
•Easy to Deploy.
•Detect different types of attacks.
Anomaly detection schemes
Misuse detection schemes
Combination of both
•Hardware / Software must be synchronized.
•Good data mining techniques
Data Mining
Definition: The semi-automatic discovery of
patterns, associations, changes, anomalies,
rules, and statically significant structures and
events in data.
Data such as,
•Failed connection attempts
•Connection delays
•Source/Destination data packets
Data Mining Algorithms
Extract knowledge in the form of models
from data.
•Classification
•Regression
•Clustering
•Association rule abduction
•Sequence Analysis
•Others
Data Mining Techniques
It allows the system to collect useful knowledge
that describes a user’s or program’s behavior
from large audit data sets.
Examples:
•Statistics
•Artificial Neural Network
•Rule Learning
•Neuro-Fuzzy
IDS Evaluation
•Rate of false positives
•Attack detection rate
•Maintenance cost
•Total cost
IDS for Mobile Wireless Systems
Designing for Wireless Networks
Problems with Wireless Networks
•Open Medium
•Dynamic changing network topology
•Lack of decentralized monitoring
•Less known security measures
•Data is harder to collect
One proposed IDS design by
Georgia Institute of
Technology
•Individual IDS agents are placed on each an every node.
 Monitors local activities
User, system and communication activities
•Nodes cooperate with each other.
 Investigate together at a broader range
•A secure communication channel among the IDS Agent.
references
•Chebrolu, S., Abraham, A., Thomas, J.P.: Feature Detection and
Ensemble Design of Intrusion Detection Systems. Computers and
security, http://dx.doi.org/10.1016/j.cose.2004.09.008
•Zhang, Y., Lee, W., and Huang, Y. 2003. Intrusion detection
techniques for mobile wireless networks. Wirel. Netw. 9, 5 (Sep.
2003), 545-556. DOI= http://dx.doi.org/10.1023/A:1024600519144
•J.P Anderson. Computer Security Threat Monitoring and
Surveillance. Technical report, James P Anderson Co., Fort
Washington, Pennsylvania, April 1980
•Eugene H Spafford. Security Seminar, Department of Computer
Sciences, Purdue University, Jan 1996.
•Biswanath Mukherjee, L Todd Heberlein and Karl N Levitt. Network
Intrusion Detection , IEEE Network, May/June 1994, pages 26-41.
Questions???